1. Field of the Invention
The present invention is related to creditworthiness data collection and aggregation, and more particularly to collecting payment history data from a large number of users by uploading data directly from installed financial or accounting software applications, aggregating such data at a central location, and generating reports and/or alerts based on the aggregated data.
2. Description of the Background Art
Assessments of creditworthiness are valuable in many business-related situations. Businesses often extend credit to other firms and/or individuals, for example by selling goods or services and billing the purchaser via an invoice for later payment. Creditors, such as vendors of products or services, wish to avoid extending credit to individuals and business entities that are likely to default on their obligations. Accordingly, most creditors perform some form of “credit check” on a potential debtor (e.g., a customer) before extending credit. Such credit checks are also performed in other situations where it is desirable to investigate the overall trustworthiness and/or ability to pay of an individual or business entity (referred to herein as a “subject company”).
Conventionally, a creditor performs a credit check by consulting a trusted provider of credit information, such as Dun & Bradstreet. At the creditor's request, the provider generates a credit report or other document that summarizes the credit history of a subject company. Based on the credit report, the creditor evaluates the creditworthiness of the subject company, and thereby makes business decisions as to whether and to what extent to extend credit to the subject company.
Credit reports generated by providers such as Dun & Bradstreet are typically based on large amounts of creditworthiness data that have been collected over a period of time. Such creditworthiness data may be based, for example, on publicly available records such as bankruptcy filings, liens, judgments and the like, summaries of the type of business and length of time the business has been in operation, complaints or comments from other vendors, and the like. In general, the creditworthiness data that is used by credit information providers is only as reliable as the techniques employed for collecting the data. To the extent that such data is collected indirectly or that the data collection relies on the efforts of individuals to accurately report their observations and interactions, the data is subject to inaccuracies. In addition, to the extent that such data is unavailable, such as for small companies or those that have not been in business a long time, the creditworthiness report may not be accurate or may not be available. Also, credit reports from providers such as Dun & Bradstreet typically attempt to cover payment behavior of businesses and fail to cover that of consumers.
Payment history is a particularly good indicator of creditworthiness, and many creditors rely on a subject company's payment history, as reported by others, in determining whether to extend credit to the company. Accordingly, credit information providers regularly obtain payment history data from vendors and other creditors, as part of their creditworthiness data collection efforts.
Conventionally, due to practical limitations, it is not feasible to collect data for every payment, or even for a large subset of payments, made by a particular subject company. Therefore, credit information providers typically collect payment history data from a subset of vendors who have dealt with the subject company. These vendors themselves do not typically report transaction-level payment data, but rather provide aggregated information about the subject company's payment patterns (e.g., number of times 30 days late). Such data is then further aggregated and extrapolated by the credit information provider in order to develop an assessment of the subject company's overall payment performance. Since the experiences of the vendor subset may not be representative of the overall behavior of the individual or company, and since in many cases the total quantity of available data for a subject company may be limited, the resultant report may suffer from inaccuracies.
What is needed, then, is a technique for expanding the scope of data collection for credit history data, so as to improve the accuracy and reliability of resultant creditworthiness reports.
What is further needed is a technique for collecting payment history data in an automated fashion, directly from vendors or other business entities, and without introducing subjective assessments of payment history, so as to further improve the accuracy and reliability of creditworthiness reports.